Conventional motion compensated temporal filtering (MCTF) reduces noise by taking a weighted average of a current (or target) frame and one or more previous (or reference) frames. When the one or more previous frames are MCTF outputs, the filter is referred to as recursive. MCTF reduces noise because (if there is no motion, or if the motion is correctly modeled and compensated) the MCTF output is a weighted average of noisy samples of the same image sample which will statistically be less noisy than a single sample. In MCTF, each sample is a blended combination of the target frame and the reference frame. Motion detection (possibly relative to a motion model) is used to decide how much of the reference frame and how much of the target frame are used for the output of MCTF. If the target frame is brighter than the reference frame, then even if a particular sample is not moving, blending in the reference sample will have a spurious effect of making the output darker.
It would be desirable to implement brightness adjusted temporal filtering.